Developing the Second Generation of Improvised Explosive Device Detector Dog
Bibliographic record
Abstract
Abstract : A number of approaches have been used to detect the presence of Improvised Explosive Devices before they detonate. One option taken by the U.S. Marine Corps has been the deployment of improvised explosive device detection dogs (IDDs). The IDD program relies on unique off-leash dog and handler teams, and uses hunt-bloodline, field-trial trained Labrador Retrievers exclusively. The research described in this report represents an important multidisciplinary effort to better characterize stress responses, cognition, and olfaction in Labrador Retrievers, drawing on the expertise of North Carolina State University (NCSU) College of Veterinary Medicine (CVM) scientists with research and clinical backgrounds in veterinary behavior, nasal toxicology, laboratory animal medicine, olfaction, and behavioral sciences. Research was performed in controlled laboratory experiments and field studies and consisted of ten distinct research phases Phase I. Evaluation of the USMC Emotional Reactivity Test Phase II. Development of an Open Field Anxiety Test Phase III. Object Discrimination Phase IV. Delayed Non Match to Position (DNMP) Phase V. Olfactory Discrimination Phase VI. Cognitive Bias Phase VII. Application of Remote Telemetry to a Novel Open Field Test of Olfaction Phase VIII. The Role of Olfactory Priming on the Detection of C4 Phase IX. Soil Depth and its Impact on Odor Detection in Dogs Phase X. Pilot Studies Examining Proton Pump Inhibitor Effects on Canine Olfaction This work resulted in an improved understanding of the strengths and weaknesses of the Emotional Reactivity Test (ERT), a primary tool used by the USMC to select candidate IDDs. Our research identified areas where the ERT was a highly effective test instrument, but also showed that the ERT was less effective as a tool for screening dogs for cognitive or olfactory abilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".